Let’s continue working with the horseshoe crab data from last time. Today, we’ll fit three models under different assumptions, evaluating their usefulness.

Case study: how do features of nesting female horseshoe crabs influence the number of males found nearby? Let’s see how carapace width influences the mean number of males nearby.

p <- ggplot(crab, aes(width, n_male)) + 
  geom_point(alpha=0.25) +
  labs(x = "Carapace Width", 
       y = "No. males\nnearby") +
  theme_bw() +
  theme(axis.title.y = element_text(angle=0, vjust=0.5))
plotly::ggplotly(p)
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>>>>>>> upstream/master

Data source: H. Jane Brockmann’s 1996 paper; found online here; another regression demo with this data is found here.

Approach 1: Estimate regression curve / model function locally

Optimize the loess fit by eye. Just modify span, to keep things simple.

<<<<<<< HEAD ======= >>>>>>> upstream/master
grid <- seq(min(crab$width), max(crab$width), length.out=100)
grid_df <- tibble(width = grid)
model1 <- loess(n_male ~ width, data=crab, degree=2, span=0.5)
grid_df %>% 
  mutate(., yhat = predict(model1, .)) %>% #. specifying new data i.e grid_df
  ggplot(aes(width, yhat)) +
  geom_line(colour="blue") +
  geom_point(data=crab, mapping=aes(width, n_male), alpha=0.25)
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>>>>>>> upstream/master

What’s the error of this model? Training error is fine.

resid1 <- crab$n_male - predict(model1)
(error1 <- mean(resid1^2))
[1] 8.64852

How well does this model answer our original question?

Approach 2: Linear Regression

Fit a linear regression model

Fit a linear regression model.

model2 <- lm(n_male ~ width, data=crab)
ggplot(crab, aes(width, n_male)) +
    geom_point(alpha=0.25) +
    geom_smooth(method="lm", se=FALSE)

Error:

<<<<<<< HEAD
model2 <- lm(n_male ~ width, data=crab)
ggplot(crab, aes(width, n_male)) +
    geom_point(alpha=0.25) +
    geom_smooth(method="lm", se=FALSE)

======= >>>>>>> upstream/master
resid2 <- crab$n_male - predict(model2)
(error2 <- mean(resid2^2))
[1] 8.716252

How well does this model answer our original question? Do you see a potential problem with this model? Are any assumptions of linear regression not true? Brainstorm ideas for how to deal with the problems.

can predict negative values. Problem if predicts negative values for reasonable widths.

How can we prevent this w/o losing interpretability?

New approaches

Let’s talk about an alternative approach called Generalized Linear Models (GLM). Topics:

A note on error comparison

Here are the errors of the above three models:

tribble(
    ~ method, ~ MSE,
    "local", error1,
    "linear", error2,
    "GLM", error3
)
>>>>>>> upstream/master

This suggests that the model function from the local method fits the data best, and that the exponential model function (GLM) fits the data poorest. This means that for prediction, the local model is best.

If our interest is interpretation, error measurements are less important. Here, the linear model is probably fine, since the model function does not drop below zero within the span of the data. Since it also fits better than the GLM, there’s probably no harm in choosing it for interpretation – unless you particularly care about the relationship for small horseshoe crabs!

Approach 4: Scientifically motivated model function?

There’s probably none here, but if there was, what then?

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=======
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YGAKCgojIyBBIG5vdGUgb24gZXJyb3IgY29tcGFyaXNvbgoKSGVyZSBhcmUgdGhlIGVycm9ycyBvZiB0aGUgYWJvdmUgdGhyZWUgbW9kZWxzOgoKYGBge3J9CnRyaWJibGUoCgl+IG1ldGhvZCwgfiBNU0UsCgkibG9jYWwiLCBlcnJvcjEsCgkibGluZWFyIiwgZXJyb3IyLAoJIkdMTSIsIGVycm9yMwopCmBgYAoKVGhpcyBzdWdnZXN0cyB0aGF0IHRoZSBtb2RlbCBmdW5jdGlvbiBmcm9tIHRoZSBsb2NhbCBtZXRob2QgZml0cyB0aGUgZGF0YSBiZXN0LCBhbmQgdGhhdCB0aGUgZXhwb25lbnRpYWwgbW9kZWwgZnVuY3Rpb24gKEdMTSkgZml0cyB0aGUgZGF0YSBwb29yZXN0LiBUaGlzIG1lYW5zIHRoYXQgZm9yIF9wcmVkaWN0aW9uXywgdGhlIGxvY2FsIG1vZGVsIGlzIGJlc3QuIAoKSWYgb3VyIGludGVyZXN0IGlzIGludGVycHJldGF0aW9uLCBlcnJvciBtZWFzdXJlbWVudHMgYXJlIGxlc3MgaW1wb3J0YW50LiBIZXJlLCB0aGUgbGluZWFyIG1vZGVsIGlzIHByb2JhYmx5IGZpbmUsIHNpbmNlIHRoZSBtb2RlbCBmdW5jdGlvbiBkb2VzIG5vdCBkcm9wIGJlbG93IHplcm8gd2l0aGluIHRoZSBzcGFuIG9mIHRoZSBkYXRhLiBTaW5jZSBpdCBhbHNvIGZpdHMgYmV0dGVyIHRoYW4gdGhlIEdMTSwgdGhlcmUncyBwcm9iYWJseSBubyBoYXJtIGluIGNob29zaW5nIGl0IGZvciBpbnRlcnByZXRhdGlvbiAtLSB1bmxlc3MgeW91IHBhcnRpY3VsYXJseSBjYXJlIGFib3V0IHRoZSByZWxhdGlvbnNoaXAgZm9yIHNtYWxsIGhvcnNlc2hvZSBjcmFicyEKCiMjIEFwcHJvYWNoIDQ6IFNjaWVudGlmaWNhbGx5IG1vdGl2YXRlZCBtb2RlbCBmdW5jdGlvbj8KClRoZXJlJ3MgcHJvYmFibHkgbm9uZSBoZXJlLCBidXQgaWYgdGhlcmUgd2FzLCB3aGF0IHRoZW4/Cg==
>>>>>>> upstream/master